code: def make_classification(
      n_samples=100,
      n_features=20,
      n_informative=2,
      n_redundant=2,
      n_repeated=0,
      n_classes=2,
      n_clusters_per_class=2,
      weights=None,
      flip_y=0.01,
      class_sep=1.0,
      hypercube=True,
      shift=0.0,
      scale=1.0,
      shuffle=True,
      random_state=None,
  ):
      """Generate a random n-class classification problem.
  
      Parameters
      ----------
      n_samples : int, optional (default=100)
          The number of samples.
  
      n_features : int, optional (default=20)
          The total number of features. These comprise `n_informative`
          informative features, `n_redundant` redundant features, `n_repeated`
          duplicated features and `n_features-n_informative-n_redundant-
          n_repeated` useless features drawn at random.
  
      n_informative : int, optional (default=2)
          The number of informative features. Each class is composed of a number
          of gaussian clusters each located around the vertices of a hypercube
          in a subspace of dimension `n_informative`. For each cluster,
          informative features are drawn independently from  N(0, 1) and then
          randomly linearly combined in order to add covariance. The clusters
          are then placed on the vertices of the hypercube.
  
      n_redundant : int, optional (default=2)
          The number of redundant features. These features are generated as
          random linear combinations of the informative features.
  
      n_repeated : int, optional (default=2)
          The number of duplicated features, drawn randomly from the informative
          and the redundant features.
  
      n_classes : int, optional (default=2)
          The number of classes (or labels) of the classification problem.
  
      n_clusters_per_class : int, optional (default=2)
          The number of clusters per class.
  
      weights : list of floats or None (default=None)
          The proportions of samples assigned to each class. If None, then
          classes are balanced. Note that if `len(weights) == n_classes - 1`,
          then the last class weight is automatically inferred.
  
      flip_y : float, optional (default=0.01)
          The fraction of samples whose class are randomly exchanged.
  
      class_sep : float, optional (default=1.0)
          The factor multiplying the hypercube dimension.
  
      hypercube : boolean, optional (default=True)
          If True, the clusters are put on the vertices of a hypercube. If
          False, the clusters are put on the vertices of a random polytope.
  
      shift : float or None, optional (default=0.0)
          Shift all features by the specified value. If None, then features
          are shifted by a random value drawn in [-class_sep, class_sep].
  
      scale : float or None, optional (default=1.0)
          Multiply all features by the specified value. If None, then features
          are scaled by a random value drawn in [1, 100]. Note that scaling
          happens after shifting.
  
      shuffle : boolean, optional (default=True)
          Shuffle the samples and the features.
  
      random_state : int, RandomState instance or None, optional (default=None)
          If int, random_state is the seed used by the random number generator;
          If RandomState instance, random_state is the random number generator;
          If None, the random number generator is the RandomState instance used
          by `np.random`.
  
      Returns
      -------
      X : array of shape [n_samples, n_features]
          The generated samples.
  
      y : array of shape [n_samples]
          The integer labels for class membership of each sample.
  
      Notes
      -----
      The algorithm is adapted from Guyon [1] and was designed to generate
      the "Madelon" dataset.
  
      References
      ----------
      .. [1] I. Guyon, "Design of experiments for the NIPS 2003 variable
             selection benchmark", 2003.
      """
      generator = check_random_state(random_state)
  
      # Count features, clusters and samples
      if n_informative + n_redundant + n_repeated > n_features:
          raise ValueError(
              "Number of informative, redundant and repeated "
              "features must sum to less than the number of total"
              " features"
          )
      if 2**n_informative < n_classes * n_clusters_per_class:
          raise ValueError(
              "n_classes * n_clusters_per_class must"
              " be smaller or equal 2 ** n_informative"
          )
      if weights and len(weights) not in [n_classes, n_classes - 1]:
          raise ValueError(
              "Weights specified but incompatible with number "
              "of classes."
          )
  
      n_useless = n_features - n_informative - n_redundant - n_repeated
      n_clusters = n_classes * n_clusters_per_class
  
      if weights and len(weights) == (n_classes - 1):
          weights.append(1.0 - sum(weights))
  
      if weights is None:
          weights = [1.0 / n_classes] * n_classes
          weights[-1] = 1.0 - sum(weights[:-1])
  
      n_samples_per_cluster = []
  
      for k in range(n_clusters):
          n_samples_per_cluster.append(
              int(
                  n_samples
                  * weights[k % n_classes]
                  / n_clusters_per_class
              )
          )
  
      for i in range(n_samples - sum(n_samples_per_cluster)):
          n_samples_per_cluster[i % n_clusters] += 1
  
      # Intialize X and y
      X = np.zeros((n_samples, n_features))
      y = np.zeros(n_samples, dtype=int)
  
      # Build the polytope
      C = np.array(
          list(product([-class_sep, class_sep], repeat=n_informative))
      )
  
      generator.shuffle(C)
  
      if not hypercube:
          C[:n_clusters] *= generator.rand(n_clusters, 1)
          C *= generator.rand(1, n_informative)
  
      # Loop over all clusters
      pos = 0
      pos_end = 0
  
      for k in range(n_clusters):
          # Number of samples in cluster k
          n_samples_k = n_samples_per_cluster[k]
  
          # Define the range of samples
          pos = pos_end
          pos_end = pos + n_samples_k
  
          # Assign labels
          y[pos:pos_end] = k % n_classes
  
          # Draw features at random
          X[pos:pos_end, :n_informative] = generator.randn(
              n_samples_k, n_informative
          )
  
          # Multiply by a random matrix to create co-variance of the features
          A = 2 * generator.rand(n_informative, n_informative) - 1
          X[pos:pos_end, :n_informative] = np.dot(
              X[pos:pos_end, :n_informative], A
          )
  
          # Shift the cluster to a vertice
          X[pos:pos_end, :n_informative] += np.tile(
              C[k, :], (n_samples_k, 1)
          )
  
      # Create redundant features
      if n_redundant > 0:
          B = 2 * generator.rand(n_informative, n_redundant) - 1
          X[:, n_informative : n_informative + n_redundant] = np.dot(
              X[:, :n_informative], B
          )
  
      # Repeat some features
      if n_repeated > 0:
          n = n_informative + n_redundant
          indices = ((n - 1) * generator.rand(n_repeated) + 0.5).astype(
              np.intp
          )
          X[:, n : n + n_repeated] = X[:, indices]
  
      # Fill useless features
      X[:, n_features - n_useless :] = generator.randn(
          n_samples, n_useless
      )
  
      # Randomly flip labels
      if flip_y >= 0.0:
          for i in range(n_samples):
              if generator.rand() < flip_y:
                  y[i] = generator.randint(n_classes)
  
      # Randomly shift and scale
      constant_shift = shift is not None
      constant_scale = scale is not None
  
      for f in range(n_features):
          if not constant_shift:
              shift = (2 * generator.rand() - 1) * class_sep
  
          if not constant_scale:
              scale = 1 + 100 * generator.rand()
  
          X[:, f] += shift
          X[:, f] *= scale
  
      # Randomly permute samples and features
      if shuffle:
          X, y = shuffle_func(X, y, random_state=generator)
  
          indices = np.arange(n_features)
          generator.shuffle(indices)
          X[:, :] = X[:, indices]
  
      return X, y
  
file_path: lightning/lightning/impl/datasets/samples_generator.py
imports: from itertools import product
  import numpy
  import scipy.sparse
  from sklearn.utils.extmath import safe_sparse_dot
  from sklearn.utils import check_random_state
  from sklearn.utils import shuffle
  from sklearn.preprocessing import MinMaxScaler
  from sklearn.preprocessing import Normalizer
label_desc: Use image node_id 3 for calling a global function with example usage: make_classification(n_samples, n_features, n_informative, n_redundant, n_repeated, n_classes, n_clusters_per_class, weights, flip_y, class_sep, hypercube, shift, scale, shuffle, random_state) and returns: X,  y